Reinforcement Learning in Healthcare

The healthcare sector has always been an early adopter and a great beneficiary of technological advances. The application of reinforcement learning, to the healthcare system, has consistently generated better results. Being a subfield of machine learning, reinforcement learning’s sole objective is to endow an individual’s skills in the behavioural decision making through the use of experience of the interaction with the world around them and create evaluative feedback. Contrary to the historically supervised learning methods which relied on one-shot, comprehensive and supervised reward indicators, the reinforcement learning approach entails a progressive decision making that is simultaneously sampled, evaluative and comes with delayed feedback. These unique features make the reinforcement learning technique an appropriate contender for developing prevailing solutions in various healthcare spheres. With this, diagnosing decisions or treatment regimens are often characterised by a lengthy and chronological procedure.

Learn how AI can transform healthcare.

Currently, machine learning, a subcategory of AI, illustrates a vital role in quite several healthcare institutions, including the advancement of innovative medical processes, management of patient information and registrations, and management of protracted ailments. Today, machine learning has given rise to practically interminable uses in the healthcare system. Also, it has greatly helped to make more efficient administrative procedures in institutions of health, personalise health treatments, map and medicate communicable diseases. 

Adoption of machine learning also affects general practitioners and healthcare systems since it is of great importance in clinical resolution sustenance, enabling prior recognition of ailments and personalised treatment strategies to warrant ideal results. With machine learning, demonstration and education of probable disease paths to patients and possible outcome, and dissimilar treatment choices are easily communicated. Moreover, it will positively impact healthcare structures in refining competence while at the same time reducing costs. Generations of acumen both to enhance the discovery of new therapeutics and ensuring the delivery of current ones will also be achieved.

There have been developments of various programs of machine learning in the healthcare systems to benefit both the sick and workers, the most common areas being:


  1. Quotient Health

Developed by Quotient Health, this software targets to lessen the expenses of assisting electronic medical records through enhancing and standardising methods through which these systems are created. It’s a definitive aim to improve the healthcare system and lower costs.

2. KenSci

KenSci uses reinforcement learning to predetermine ailments and treatments to help medical practitioners and patients intervene at earlier stages. Moreover, it helps in the prediction of population health threats through pinpointing patterns, growing precarious markers, model disease advancement, among others.

3. Ciox Health

Ciox Health adopts the use of machine learning to improve health data control and altercation of health data to streamline workflows. Also, it promotes and facilitates the right of entry to clinical statistics and improves the precision and movement of health data.

Medical Imaging and Diagnostics

  1. Pathai

PathAI has a great technology that uses ML to aid pathologists to make a faster and more precise diagnosis. Furthermore, it helps clinicians establish patients who might be beneficiaries of a new type of treatment or therapy.

2. Quantitative Insights

With its computer-assisted breast MRI workstation Quantx, Quantitative Insights aims at improving the swiftness and precision of breast cancer identification. Its main objective is to enhance outcomes for victims through a value-added diagnosis by radiologists.

Microsoft InnerEye

3. Microsoft

Microsoft developed the Project InnerEye, which uses MI to distinguish amid tumours and healthy framework by use of 3D radiological representation. This greatly helps medical specialists in radiotherapy, planning of surgical procedures, among others.

Discovery and Growth

  1. Pfizer

Thanks to IBM’s Watson AI expertise, Pfizer has been able to adopt the use of MI for immune-oncology research on how an individual’s immune structure can combat cancer.  It has also been at the forefront in the development of an AI-driven platform to clone small-molecule medicaments as part of their innovation and advancement efforts. This project is expected to integrate quantum processes and ML to aid in the extrapolation of the pharmacological attributes of a wide assortment of molecular composites.

2. Insitro

This contemporary startup combines ML and information science with cutting-edge laboratory expertise to develop drugs. Its main aim is to ensure access to quick curing and less costly drugs.

3. Biosymetrics

Through the use of its ML mechanism Augusta, Biometrics gives customers a chance to execute automatic ML and pre-processing of information. This, in turn, improves precision and eradicates an inefficient task which is usually done by humans in diverse segments of the medical system comprising biopharmaceuticals, technology, precision medicine, among others.

Medical Data

  1. Concerto Health AI

Concerto Health AI adopted the use of ML to scrutinise oncology information, provide acumens that permit oncologists, pharmacological establishments, customers and health providers to exercise accuracy in medicine and well-being.

2. Orderly Health

Orderly Health prides itself on the use of machine learning to develop an automatic 24/7 curator for healthcare through email, text, or video conferencing. Its main objective is to aid insurers, and healthcare establishments in cutting costs and time by facilitating processes for individuals to realise their privileges and trace the least costly providers. Moreover, it enables employees and other affiliates to recognize their benefits easily.

Treatment and Prediction of Diseases

  1. Prognos

With the aid of machine learning, Prognos AI platform simplifies timely disease detection, identifies therapy requirements, selects opportunities for clinical trials, highlights any gaps in the healthcare system and other important factors for several conditions.

2. Beta Bionics

Beta Bionics is known for evolving a cloth-able bionic pancreas known as iLet, which helps in the management of blood sugar intensities in patients who have Type 1 diabetes.

Recent approaches have yielded several barriers that exist with the application of reinforcement learning to the health care system. The fundamental challenges which are believed to be facing adoption of reinforcement learning include:

a. The impracticality of learning and evaluating purely observational data

Due to ethical and logistical reasons, it might not be possible to evaluate healthcare policies and make decisions based on outcomes that have just been averagely computed with no specific metrics.

b. Partial observability

Contrary to other popular belief, in medicine, it is almost impossible to observe everything taking place in an individual’s body. Not all signals will provide the ground truth about a patient. In such cases where sufficient data is not available, medical practitioners depend on calculated estimates.

Prediction of disease through reinforcement learning

Reward Function Concern

It’s often challenging to find a reward function that will balance temporary improvement with overall lasting success. With this, it’s difficult to determine which actions gave rise to the reward.

c. Issues of non-stationary data

By nature, healthcare data is itinerant and dynamic. Over time, treatment objectives are likely to change and evolve in a dynamic way that was not previously observed in the training data.

While reinforcement learning has led to great improvements in therapeutic development, diagnostics, and treatment commendations, there have also been several setbacks. As much as machine learning continues to offer the transformative potential for health and healthcare systems, some criticism revolving around it is highly merited as discussed below.

d. Data quality is critical yet overlooked

The quality of data obtainable to generate findings is usually dependent on the statistical procedures used and is also the key to success. Unsatisfactory data will not yield significant insights. Regardless of the sophistication of the analytical methods used, there are often some shortfalls in data adequacy. As much as there are high expectations with machine learning, it also has these shortcomings. 

e. The results of machine learning must be reproducible

Algorithms of machine learning often perform better than other conventional arithmetical methodologies. Due to this, there is often a risk that the results will not be indicative of true or underlying causal processes.

f. Algorithms must be transparent

One of the most noticeable criticisms of machine learning methods is the fact that it represents a black box and offers no clear understanding of how acumens are generated. This brings about the risk of alchemy whereby users won’t understand why some algorithms work while others fail or the indicators used in selecting amongst different algorithm configurations.

g. Algorithms ought to be credible

The algorithms of machine learning must offer acumens which are reliable and associated with the scientific or clinical accord. If it fails to replicate established findings or conflicts with the proven indications, it’s more likely to be a methodological inaccuracy.

h. Algorithms must demonstrate impact

Since machine learning uses gains in performance compared to predictable statistical methodologies as grounds for claims of improvement, this approach is not always the correct standard. Testing the impact of the machine learning algorithm is always very important.

Reinforcement learning is a thrilling scope in the world of healthcare with its ability to regulate ultimate behaviours within a specific setting. Its adoption leads to a more detailed and accurate treatment at reduced costs.

Reinforcement learning, if well adopted is believed to bring about critical results in the coming years and will greatly impact the care and control of prevalent chronic ailments in influencing patient-centred health information with external influences comprising weather and economic dynamics or pollution exposure. It will proficiently generate precise medicine solutions personalised to individual features by using available genetic information to uncover the best conceivable medical treatment strategies.

Evidently, Reinforcement learning and other such machine learning algorithms are creating quite a wave across different industries. Upskill in this domain and become part of this technological revolution. Visit Great Learning to learn more about the different courses on machine learning.



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